Culturally Tuned Multilingual AI Infrastructure
Budget: $10,000 – $20,000 USD
I’m assembling a cross-functional team to build a multilingual AI infrastructure, taking inspiration from models such as Deepseek but pushing much further on cultural relevance. That single priority drives every technical decision: from model architecture to data pipelines and user-facing apps.
To achieve this I want us to:
• weave local dialects and minority languages directly into the tokenizer and pre-training corpus,
• curate or create culturally specific datasets that reflect real idioms, context and social norms, and
• keep a constant feedback loop with local academics, translators and community experts throughout development and evaluation.
The scope spans the full stack of an AI product. AI/NLP engineers will experiment with fine-tuning strategies and alignment techniques; language data engineers will own collection, cleaning and augmentation of text and speech corpora; backend and API developers will expose model capabilities securely; mobile and web developers will craft lightweight, accessible clients; and DevOps/MLOps engineers will automate training, versioning and scalable deployment to keep costs predictable.
Core deliverables I expect:
1. A pre-trained foundation model with culturally aligned checkpoints and evaluation reports.
2. A multilingual dataset repository with documented provenance and licensing.
3. REST and gRPC APIs serving both text and speech endpoints, protected with OAuth2.
4. Reference mobile (Flutter or React Native) and web (React/Next.js) clients demonstrating latency under one second in target regions.
5. A CI/CD pipeline (Docker, Kubernetes, Terraform preferred) that can spin up training or inference clusters on demand, complete with monitoring and cost dashboards.
Target regions are still being finalised; we’ll decide them together once early data audits are complete, so flexibility and prior internationalisation experience are advantageous.
If you have a track record shipping NLP systems, handling multilingual data, or scaling GPU workloads efficiently, I’d love to see how you can slot into this ambitious build.
To achieve this I want us to:
• weave local dialects and minority languages directly into the tokenizer and pre-training corpus,
• curate or create culturally specific datasets that reflect real idioms, context and social norms, and
• keep a constant feedback loop with local academics, translators and community experts throughout development and evaluation.
The scope spans the full stack of an AI product. AI/NLP engineers will experiment with fine-tuning strategies and alignment techniques; language data engineers will own collection, cleaning and augmentation of text and speech corpora; backend and API developers will expose model capabilities securely; mobile and web developers will craft lightweight, accessible clients; and DevOps/MLOps engineers will automate training, versioning and scalable deployment to keep costs predictable.
Core deliverables I expect:
1. A pre-trained foundation model with culturally aligned checkpoints and evaluation reports.
2. A multilingual dataset repository with documented provenance and licensing.
3. REST and gRPC APIs serving both text and speech endpoints, protected with OAuth2.
4. Reference mobile (Flutter or React Native) and web (React/Next.js) clients demonstrating latency under one second in target regions.
5. A CI/CD pipeline (Docker, Kubernetes, Terraform preferred) that can spin up training or inference clusters on demand, complete with monitoring and cost dashboards.
Target regions are still being finalised; we’ll decide them together once early data audits are complete, so flexibility and prior internationalisation experience are advantageous.
If you have a track record shipping NLP systems, handling multilingual data, or scaling GPU workloads efficiently, I’d love to see how you can slot into this ambitious build.